Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm
AbstractIn recent years, developing countries, especially resource-dependent regions, have been facing the paradox of ensuring both emissions reduction and economic development. Thus, there is a strong political desire to forecast carbon emissions reduction potential and the best way to achieve it. This study constructs a methodology to assess carbon reduction potential in a resource-dependent region. The Simulated Annealing Programming algorithm and the Genetic algorithm were introduced to create a prediction model and an optimized regional carbon intensity model, respectively. Shanxi Province in China, a typical resource-dependent area, is selected for the empirical study. Regional statistical data are collected from 1990 to 2015. The results show that the carbon intensity of Shanxi Province could drop 18.78% by 2020. This potential exceeds the 18% expectation of the Chinese Government in its ‘13th Five-Year Work Plan’ for Controlling Greenhouse Gas Emissions. Moreover, the carbon intensity of the province could be further reduced by 0.97 t per 10,000 yuan GDP. The study suggests that the carbon emissions of a resource-dependent region can be reduced in the following ways; promoting economic restructuring, upgrading coal supply-side reform, perfecting the self-regulation of coal prices, accelerating the technical innovation of the coal industry, and establishing a flexible mechanism for reducing emissions. View Full-Text
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Li, W.; Li, G.; Zhang, R.; Sun, W.; Wu, W.; Jin, B.; Cui, P. Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm. Sustainability 2017, 9, 1161.
Li W, Li G, Zhang R, Sun W, Wu W, Jin B, Cui P. Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm. Sustainability. 2017; 9(7):1161.Chicago/Turabian Style
Li, Wei; Li, Guomin; Zhang, Rongxia; Sun, Wen; Wu, Wen; Jin, Baihui; Cui, Pengfei. 2017. "Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm." Sustainability 9, no. 7: 1161.
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